A traits-based approach to assess aquaculture’s contributions to food, climate change, and biodiversity goals

Aquaculture has the potential to support a sustainable and equitable food system in line with the United Nations Sustainable Development Goals (SDG) on food security, climate change, and biodiversity (FCB). Biological diversity amongst aquaculture organisms can drive diverse contributions to such goals. Existing studies have assessed the performance of a limited number of taxa in the general context of improving aquaculture production, but few explicitly consider the biological attributes of farmed aquatic taxa at the FCB nexus. Through a systematic literature review, we identify key traits associated with FCB and evaluate the potential of aquaculture to contribute to FCB goals using a fuzzy logic model. The majority of identified traits are associated with food security, and two-thirds of traits linked with food security are also associated with climate change or biodiversity, revealing potential co-benefits of optimizing a single trait. Correlations between FCB indices further suggest that challenges and opportunities in aquaculture are intertwined across FCB goals, but low mean FCB scores suggest that the focus of aquaculture research and development on food production is insufficient to address food security, much less climate or biodiversity issues. As expected, production-maximizing traits (absolute fecundity, the von Bertalanffy growth function coefficient K, macronutrient density, maximum size, and trophic level as a proxy for feed efficiency) highly influence a species’ FCB potential, but so do species preferences for environmental conditions (tolerance to phosphates, nitrates, and pH levels, as well as latitudinal and geographic ranges). Many highly farmed species that are typically associated with food security, especially finfish, score poorly for food, climate, and biodiversity potential. Algae and mollusc species tend to perform well across FCB indices, revealing the importance of non-fish species in achieving FCB goals and potential synergies in integrated multi-trophic aquaculture systems. Overall, this study provides decision-makers with a biologically informed assessment of desirable aquaculture traits and species while illuminating possible strategies to increase support for FCB goals. Our findings can be used as a foundation for studying the socio-economic opportunities and barriers for aquaculture transitions to develop equitable pathways toward FCB-positive aquaculture across nuanced regional contexts.


Systematic review protocol (link to file) 1.2 PRISMA Documentation
Table S1.PRISMA checklist (link to file) Table S2.PRISMA flowchart (link to file)

Inclusion and exclusion criteria
Table S3.Inclusion criteria for publications to be included in the SLR Table S4.Traits from the literature review which were excluded from the fuzzy expert system 1.4 Coding and synthesizing data for FCB Themes Figure S1.Themes synthesized during systematic review 1.5 References and data for systematic review 1.5.1 Studies included in systematic review (link to file) 1.5.2Data extraction from systematic review (link to file)

Selecting species for analysis
Table S5.Species proxies

Calculations of input trait data 3.1 Calculation of ranges for temperature, nitrate, phosphate, salinity and pO2 3.2 Calculation of geographic range (water area) and latitudinal range
Table S6.Rules linking FAO area and latitudinal range with new linguistic categories for geographic range Table S7.Corresponding water area values in km² for each linguistic category of geographic range

Assignment of strength of spatial behavior
Table S8.Keywords that describe the spatial behavior of aquaculture species and their corresponding multiplication factors

Micronutrient and macronutrient density
Table S9.Average RDA of 5 micronutrients and 2 macronutrients for children under 5 Table S10.Linguistic categories for micronutrient (a) and macronutrient (b) density, with corresponding percent contributions of each nutrient in 100 grams wet weight to RDA.

Taxonomic group-based traits
Table S11.Trophic levels assigned to species (based on taxonomic groups) when information was unavailable in FishBase and SealifeBase Table S12.Linguistic assignments for 5 traits based on taxonomic groupings (1-Fish, 2-Crustaceans, 3-Molluscs, 4-Algae) 3.6 Reproductive frequency 3.7 Data References (link to file) 4. Fuzzy expert system

Fuzzy membership
Table S13.Trait fuzzy sets and corresponding FCB contribution potential (link to file) Figure S2.Fuzzy membership functions for traits used in our expert system 4.2 Example calculations 4.2.1 Example calculation of a species' final degree of membership 4.2.2An illustrative example 4.3 Non-parametric species rankings Table S14.Non-parametric species rankings (link to file)

Statistical tests on non-normal distributions of FCB scores
Table S15.Shapiro-Wilk normality test results Table S16.Kruskal-Wallis test results Table S17.Kruskal-Wallis multiple comparison using post-hoc Dunn's test 5. Sensitivity analysis Figure S3.Deviations in index values from the baseline estimate (all traits included) of food security (a, b), climate change (c, d), and biodiversity (e, f) following removal of traits as inputs to the fuzzy system ______________________________________________________________________________

Systematic literature review
We chose a qualitative review because our aim was to assess the potential of aquaculture species to contribute to FCB goals, and we felt this required an inclusive approach to deriving associations, one that would use existing knowledge to tell us about species' potential, without requiring absolute certainty about the strength of the relationship between traits and FCB outcomes.For our systematic review, we searched for qualitative descriptions of traits and their associations with FCB, and used words and text (anywhere from a sentence to a paragraph) to summarize and explain our findings.Text is often used as raw data for knowledge-building in qualitative systematic reviews 36 .Associations between traits and FCB did not always appear in the literature as primary findings, and did not depend on study design--components that are evaluated during a critical appraisal of quantitative studies 37 .Identifying trait-FCB associations involved making some inferences about the relationships of traits with FCB.For example, if one source mentioned that filter-feeding provides a "regulating service" by reducing eutrophication through uptake of excess nutrients, and another source mentioned filter-feeding along with the key word "biodiversity", then we were able to infer a linkage between the trait and biodiversity using multiple observations.Inferences are common in both qualitative and quantitative research, and King et al. (1995) argue that "uncertain inferences are every bit as scientific as more certain ones so long as they are accompanied by honest statements of the degree of uncertainty accompanying each conclusion" 38 .We do not search for or state the strength of the relationship between traits and FCB using information collected in the review, as there can be inconsistency and uncertainty in trait values and FCB outcomes--making associations based on a broad range of sources with varying data availability tricky to derive.Rather, we use the fuzzy logic framework to explicitly include ranges in uncertainty, and we validate our results using appropriate techniques for qualitative reviews such as saturation and fit 39 , which refers to the way findings are linked and interact to form a whole picture 40 .We kept track of new insights using a saturation curve, and achieved fit by "filling in the blanks" as we generated understanding about how traits overlap across FCB categories and interact via cross-cutting FCB themes.That fit is a key indicator of validity in qualitative reviews also demonstrates the suitability of this approach to our research at the intersection of food, climate and biodiversity.

Inclusion and exclusion criteria
Table S3.Inclusion criteria for publications to be included in the SLR

Criteria Decision
When the predefined keywords exist in the specified section (title, abstract, text) Inclusion

The paper is available in English Inclusion
The paper is published in a scientific peerreviewed journal or is a form of gray literature such as a government report, dissertation or conference paper Inclusion The paper is a study focused on species which are used in aquaculture Inclusion The paper describes functional aquaculture traits Inclusion The paper address at least one of FCB as a challenge, goal, opportunity, etc. Inclusion

Excluded traits
We refined the list of traits by selecting explicit, functional traits for which data could be found.Since differing production and environmental contexts including genetic modification, pollution, recirculation tanks, and novel feeding strategies can alter the FCB potential of species 12,34,35 and are hard to control for when analyzing a broad range of species, we chose attributes that are inherent to a species.Our assumption during analysis was that all species were subject to the same conditions.Thus, the inherent traits revealed more about the actual species' FCB potential and less so about aquaculture technology.Very broad characteristics such as "complexity of life cycle" were broken down into more specific attributes, while very specific traits were grouped into attributes of a more descriptive and appropriate scale.The final list of traits (Figure 1) was used to create the fuzzy expert system.
The following traits which appeared in the literature search were excluded from the fuzzy expert system because they were not explicit functional traits: Understanding the relationship between species traits and ecosystem functioning is crucial before using traits as indicators 1 .Synthesis allowed us to refine our definitions of food security, climate change and biodiversity goals and improve our understanding on how traits were related to these goals.We coded the data extracted from each of the 152 publications into four categories: food security, climate change, biodiversity, and "interconnected", for traits that fit multiple categories.Traits in the latter category also helped explain the relationship between F, C and B categories.
Based on the traits that were grouped together, we were able to synthesize themes or "desirable outcomes" for each FCB goal.These "desirable outcomes" bolster the FAO definitions of food security, climate change and biodiversity and further contextualize the importance of species' biological and ecological characteristics.Desired outcomes for food security include consistent, accessible, and efficiently produced aquaculture to support the long-term needs of communities 2,3,4,5,6 .Aquaculture can contribute to both direct and indirect forms of food security by providing nutrition, as well as income 7 .According to the literature, desired outcomes for aquaculture related to climate change are adaptation to global change and reduction of aquaculture's environmental footprint for resilient production 8,9,10,11 .The desired biodiversity outcome of aquaculture was the protection of wild, especially native species and ecosystems through resource use efficiency and reducing dependency on wild stocks for feed 12,13,14,15 .
By organizing traits under these themes, the linkages between traits and desired FCB outcomes became clear.For example, traits associated with food security were generally related to a species' suitability for mass culture under variable environmental conditions, nutritional density and resource use efficiency.Traits associated with climate change had to do with a species' environmental robustness, efficiency in producing nutrition, and its climate change mitigation potential.For biodiversity, traits usually indicated the risk of species becoming invasive as well as the biodiversity footprint of its resource use and potential to improve environmental quality.
For traits that were coded as "interconnected", synthesis yielded three major themes about how issues of food security, climate change and biodiversity are related.These themes helped to contextualize traits that appeared in multiple FCB categories.Traits associated with both food security and climate change revealed the goal of "producing accessible, efficient nutrition in a changing world", while traits linked to both food security and biodiversity revealed the importance of "producing nutrition with minimal environmental impacts".A third theme, "synergies and trade-offs between climate change and biodiversity" emerged from traits in the respective categories (Figure S1).Synthesized themes and definitions acknowledge the interconnectedness of food security, climate change and biodiversity.We refined the list of traits by selecting explicit, functional traits for which data could be found.Since differing production and environmental contexts including genetic modification, pollution, recirculation tanks, and novel feeding strategies can alter the FCB potential of species 12,34,35 and are hard to control for when analyzing a broad range of species, we chose attributes that are inherent to a species (See Table S4 for list of omitted traits).Our assumption during analysis was that all species were subject to the same conditions.Thus, the inherent traits revealed more about the actual species' FCB potential and less so about aquaculture technology.Very broad characteristics such as "complexity of life cycle" were broken down into more specific attributes, while very specific traits were grouped into attributes of a more descriptive and appropriate scale.The final list of traits (Figure 1) was used to create the fuzzy expert system.See Supplementary Information 3 for additional details about strategies to collect and calculate trait data.

Selecting species for analysis
We analyzed 54 major aquaculture species (by production) listed by the Food and Agriculture Organization (FAO) in the 2022 State of World Fisheries and Aquaculture 14 .The FAO lists the 15 most-farmed inland and coastal/marine finfish species, and the top eight farmed crustacean, mollusc and algae species.This relatively small set of "staple" species comprise the vast majority of aquaculture production 14 .For example, the top 15 farmed inland finfish species represent 79.3 percent of the global inland aquaculture production in 2020 and grass carp by itself accounts for 11.8 percent percent of production in the same category.In marine and coastal aquaculture (2020), Atlantic salmon makes up 32.6 percent of global production.This percentage rises to 77 percent when 14 other major marine and coastal finfish species are considered.Eight major species are responsible for a high 95.3 percent of crustacean production, with whiteleg shrimp representing the largest proportion of 51.7 percent.In mollusc aquaculture, 84 percent of production is driven by eight species including cupped oysters (30.7 percent) while algae production (93.7 percent) is dominated by just eight species, led by Japanese kelp (35.5 percent).Since Nile tilapia and rainbow trout were included as major species in both inland and marine/coastal production, we used distinct annual production values to weight their FCB potential scores.
FAO reports data for "species items" including individual species, finfish hybrids, and groups of species identified at the genus, family or higher levels 14 .Rather than omitting highly-produced species from analysis, we opted to use species proxies when data were only available at the genus level or higher (See Table S1).To find reasonable proxies, we chose commonly farmed species that appeared on the FAO Aquatic Species Fact Sheets after searching for the genus or family listed on FAO aquaculture species database (https://www.fao.org/fishery/en/culturedspecies/search).This resulted in 54 unique species across four broad taxonomic groups (finfish, crustaceans, molluscs, algae).We used the specieslevel proxies to search for trait data on Fishbase and SealifeBase.
Table S5.Species proxies used for analysis when data were only available at the genus or higher taxonomic classification.

SOFIA entry Proxy
Inland finfish Carassius spp.

Calculations of input trait data
Most biological and ecological trait data were available on Fishbase and SealifeBase for the 54 analyzed species.Information for some traits were not included in either database, and non-fish species in particular lacked data on traits such as growth rate and fecundity.When key traits associated with FCB were not available in Fishbase or SealifeBase we calculated values for each species based on information available in additional databases such as the Ocean Biodiversity Information System (OBIS), Global Biodiversity Information Facility (GBIF), World Ocean Atlas (WOA18) and datasets from the Nereus Program.
To find environmental range data we used published methods and species occurrence data to calculate plausible species tolerances to pO2, temperature, nitrate, phosphate, salinity as well as their latitudinal range and water area occupied.Lower and upper quartiles of each trait (25th and 75th percentiles, respectively) were used to calculate the fuzzy ranges (low, medium, high and very high overlapping categories).Lack of data was penalized.When no trait data were published for a species and calculations could not be performed to elucidate trait information, the species was assigned to the "low potential" linguistic category for the attribute.Unless otherwise specified, we used quartiles of each trait's data distribution (25th, 50th, 75th percentiles) to inform the fuzzy set thresholds.

Calculation of ranges for temperature, nitrate, phosphate, salinity and pO2
To calculate species ranges for temperature, nitrate, phosphate and salinity, we compared global species occurrence data from GBIF and OBIS with WOA18 data for monthly averages of the four variables.Ranges were defined as the minimum (first percentile) and maximum (99th percentile) values for each ocean variable where the species can be found.
While dissolved oxygen in seawater has often been used to represent a species' oxygen range, the partial pressure of O2 (pO2) in seawater provides a more nuanced indicator of where a species can live.This is because oxygen content in water is highly influenced by temperature, and pO2 can reveal temperature-dependent O2 thresholds 16,17 .Species-specific ratios between pO2 supply and demand, also known as the Aerobic Growth Index (AGI) 16 , can also be used as an index of habitat viability 17 .With the formula outlined in Morée et al. (2022), we used salinity, temperature (mean of sea surface and 200 meters) and dissolved oxygen data (WOA18) to calculate the partial pressure of oxygen in seawater.We then used species occurrence data from GBIF and OBIS to find global species pO2 thresholds, defined as the pressure (in mbar) at which 10 percent of the species data occur (10th percentile).

Calculation of geographic range (water area) and latitudinal range
Geographic range in terms of the area of the ocean surface (km²) corresponding with where a species lives, as well as latitudinal range, reveal how broadly a species can live.To calculate spatial ranges, we opted to use Fishbase species distribution data which are inputs to the dynamic bioclimate envelope model (DBEM) developed by Cheung et al. ( 2008) 18 .Using ocean surface area data on a 0.5° × 0.5° resolution grid, we summed the area in each cell where a given species had distribution or occurrence data to find the amount of viable ocean area in square kilometers for each species.Minimum and maximum latitudes of each species' distribution were used to find the latitudinal range.
Species distributions are much larger than represented by occurrence data from GBIF and OBIS, but not all of our species are included in the distribution data.For those species lacking distribution information, we substituted occurrence data from OBIS to find latitudinal range and developed a scheme to calculate geographic range based on latitude information and FAO area data.Using Fishbase and SealifeBase, we found the total shelf area of the FAO areas each species is native to.We then calculated the percentage of inland waters and/or ocean areas in the species' range based on the FAO shelf area.Using rules that take into account both latitudinal range (from OBIS) and ocean/inland percentage (Table S2), we classified species into a preliminary fuzzy set (linguistic category from low to very high geographic range) which was then translated into a new value (in km²) that placed the species at the appropriate level (low to very high) amongst the other species which had distribution data (Table S3).
We used the following rules to transform latitudinal range and FAO area into geographic range (water area): Table S6.Rules linking FAO area and latitudinal range with new linguistic categories for geographic range.60° was chosen as the latitudinal range threshold between linguistic categories since it is the mean latitudinal range.

IF total FAO area % is: AND latitudinal range is:
Geographic range is: Very High 15,000,000

Assignment of strength of spatial behavior
To assign aquaculture species a score for spatial behavior, we looked for keywords in Fishbase and SealifeBase that describe the spatial behavior of species (Table S4) and followed the method in Cheung et al. ( 2005) which assumes a baseline spatial behavior strength of 1 for species forming groups, 40 for species forming aggregations or shoals, and 80 for schools 19 .We then multiplied the baseline spatial behavior strength (B) by adjustment factors (A) determined by the linguistic descriptions available for each species: S is the total spatial behavior strength between 1 and 100 and n is the number of keywords included.
Due to their high population densities, sessile species such as those belonging to mollusc and algae taxonomic groups were assigned high spatial behavior scores (> 60) that placed them in the high and very high categories.
Table S8.Keywords that describe the spatial behavior of aquaculture species and their corresponding multiplication factors

Micronutrient and macronutrient density
We extracted species data for protein and omega-3 polyunsaturated fatty acids (grams per 100 grams) from Fishbase and Sealifebase.Protein and fats are oftentimes the only nutrients included in food security indices, despite the importance of micronutrient intake for adequate nutrition 20 .We used data from Fishbase for calcium, iron, selenium, vitamin A and zinc densities, which are crucial for human health yet have low levels of adequate intake globally 21,22 .Following Maire et al. ( 2021), we defined micronutrient density as the percent contribution of 100 grams of wet weight to the recommended dietary allowance (RDA), the daily intake level that meets the dietary needs of 97-98% of the population 23,41 .While we used the average RDA for children under 5 years old (Table S5), our results are likely to apply across age groups because recommended dietary allowance between the population group we used and the rest of the population are strongly correlated 24 .The percent RDA contribution of each of the 5 micronutrients in 100g of wet weight were summed together with a maximum of 100% per nutrient, for a maximum of 500% RDA total.Capped percentage contributions to RDA prevent extreme values from skewing the variation in micronutrient density scores.
We consulted the Federal Drug Administration's Percent Daily Value index to help inform the fuzzy sets (low, medium, high, very high) we used to classify species based on nutrient density (Table S6).Percent Daily Value indicates whether a serving of food is high (20 percent or more) or low (5 percent or less) in a particular nutrient 25 .
Table S9.Average RDA of 5 micronutrients and 2 macronutrients for children under 5 (6 months to 5 years, since < 6 months assumed to be breastfed).We used a conservative bioavailability of 5% for iron to reflect a broad population which may include those with limited consumption of vitamins that increase the bioavailability of iron, those consuming plant-based sources of iron with lower bioavailability rates, and those with low iron stores.Pre-existing iron status has been shown to greatly influence further absorption of iron 25 .
Vitamin Nutrient data were more difficult to find for non-fish species.We used unpublished micronutrient data for invertebrate species calculated by co-author Christina Hicks.For algae species we found macro-and micronutrient data from the literature and converted dry weight nutrient content into wet-weight values 27,28,29 .

Taxonomic group-based traits
Some traits like trophic level, pH sensitivity, and carbon sequestration, ocean acidification buffering, bioremediation and structural provisioning potential were assigned based on each species' taxonomic group.When trophic level was not available in FishBase or SealifeBase, the following assignments were made: Table S11.Trophic levels assigned to species (based on taxonomic groups) when information was unavailable in FishBase and SealifeBase 1 Algae 1.5 Filter-feeders (ex: bivalves) 2 Herbivores

4+ Apex predators
We grouped species into one of four broad taxonomic categories (1-Fish, 2-Crustaceans, 3-Molluscs, 4-Algae) to categorize their pH sensitivity and potential for carbon sequestration, ocean acidification buffering, bioremediation and structural provisioning.The rules in Table S8 are based on the literature 30,31 and were used to assign species linguistic values for each of the 5 traits based on their taxonomic grouping.For example, we assumed high (membership = 0.25) to very high (membership = 0.75) sensitivity to PH for molluscs (row 1 of Table S8).

Reproductive frequency
Arbitrary categories were created for reproductive frequency from "once/lifetime" to "several times/year" in order to accommodate species in every taxonomic category."Perennial" algae species were assigned to "year-round" while "annual" species were categorized as "once/year" under the new arbitrary levels.Graph a shows output fuzzy sets for the FCB potential of aquaculture species, with potential scaled from 1 to 100.Some traits had opposite effects on FCB outcome (high aggression = low food security potential).Salinity range (f) is unitless.VGBF (q) stands for Von Bertalanffy Growth Function.The coefficient K describes how quickly a species can reach maximum length.We log-transformed the scale for absolute fecundity (o) and geographic range (g).Trophic level serves as a proxy for feed efficiency (i) and aggression (p).See Table S13 for corresponding FCB fuzzy sets for all traits.where AccMem is the degree of membership to a fuzzy set (e.g., to low food security potential) of conclusions from trait i or after combining conclusions from multiple traits.ℎ₍ᵢ₊₁₎ denotes the membership of the trait being accumulated into the final degree of membership.

Data References
To accumulate the "low" conclusions from the illustrative example above: AccMem (VBGF K and absolute fecundity) = 0.26 + (0.58 x (1-0.26))= 0.69 To accumulate the "medium" conclusions from the illustrative example above: AccMem (VBGF K and macronutrient density) = 0.42 + (0 x (1-0.42))= 0.42 Since the membership of the macronutrient density value to the medium contribution set (0.19) is smaller than the threshold of 0.2, the membership value is transformed to zero (Cheung et al.  2005).
The calculation to defuzzify the membership values from "low" and "medium" food security contribution potential categories would be: FinInd= [(0.69x 1) + (0.53 x 25)] / (0.69 + 0.53) = 11.43 (this example conclusion is quite low because only the low and medium contribution levels were defuzzified).

An illustrative example
The fuzzy system calculated scores along three indices (food security, climate change and biodiversity potential) for 54 major aquaculture species.The example below shows the calculation of food security scores for Atlantic salmon, an economically important aquaculture species in British Columbia:

Sensitivity analysis
Jackknife analysis showed that median deviations in the estimated indices for food security (Figure S3a), climate change (Figure S3c) and biodiversity (Figure S3e) were very small when individual traits were removed from the fuzzy system.Upper and lower quartiles (25% and 75%) of the deviations in indices were usually within 2 points (maximum of 100) relative to the baseline estimates when all traits were included for food security, and within 5 points for climate change and biodiversity.
The food security index was most sensitive to absolute fecundity, macronutrients, maximum size, phosphate range (Figure S3a).Phosphorus range was included as a trait in the food security fuzzy system because the presence of nutrients such as phosphorus and nitrates is common in high-density rearing environments and tolerance to high nutrient levels can be beneficial for production 33 .The deviations in the food security index following the removal of these traits are greater than 10 index points in one or both directions.Removal of absolute fecundity and maximum size tended to result in unsymmetrical negative bias on the predicted food security index.In contrast, excluding macronutrients or phosphate range tended to result in positive bias.The food security index was moderately sensitive to VBGF (K) and nitrate range.Removal of these traits resulted in deviations greater than 5, but less than 10 index points.
The climate change index was most sensitive to feed conversion efficiency (using trophic level as a proxy), nitrate range and sensitivity to pH, with deviations greater than 10 index points in either direction (Figure S3c).The exclusion of each of these three traits from the system resulted in a negative bias on the climate change index.Removing pH sensitivity resulted in deviations up to -20 compared to the baseline.The climate change index was moderately sensitive to the removal of phosphate range, pO2 tolerance and salinity range.
The biodiversity index was most sensitive to absolute fecundity, latitudinal range, geographic range, and VBGF (K) (Figure S3e).Removal of each of these traits resulted in unsymmetrical negative bias on the biodiversity index, with deviations greater than 10.Deviations neared -20 when fecundity and VBGF (K) were removed from the system.In contrast, the removal of taxonomic-based traits such as bioremediation potential and structural provisioning potential resulted in a positive bias on the biodiversity index.
When we randomly removed an increasing number of attributes from the system the index value deviations of food security (Figure S3b), climate change (Figure S3d) and biodiversity (Figure S3f) increased compared with the baseline estimates.The medians of deviated index values for all three indices stayed around zero due to symmetrical deviations (positive and negative biases) following the removal of traits.However, maximum deviations increased to greater than -40 index points when four or more traits were excluded from index calculations of climate change and biodiversity (Figure S3d and S3f).Maximum deviations increased to greater than 20 points in either direction when three or more traits were excluded from index calculations of food security (Figure S3b).
When we randomly removed an increasing number of attributes from the system the index value deviations of food security, climate change and biodiversity increased compared with the baseline estimates (Figure S3).The medians of deviated index values for all three indices stayed around zero due to symmetrical deviations (positive and negative biases) following the removal of traits.However, maximum deviations increased to greater than -40 index points when four or more traits were excluded from index calculations of climate change and biodiversity (Figure S3d and S3f).Maximum deviations increased to greater than 20 points in either direction when three or more traits were excluded from index calculations of food security (Figure S3b).

Figure S2 .
Figure S2.Fuzzy membership functions for traits used in our expert system (L = Low, M = Medium, H = High, VH = Very high).For maximum size (d), S = Small, M = Medium, L = Large, VL = Very large.Graph a shows output fuzzy sets for the FCB potential of aquaculture species, with potential scaled from 1 to 100.Some traits had opposite effects on FCB outcome (high aggression = low food security potential).Salinity range (f) is unitless.VGBF (q) stands for Von Bertalanffy Growth Function.The coefficient K describes how quickly a species can reach maximum length.We log-transformed the scale for absolute fecundity (o) and geographic range (g).Trophic level serves as a proxy for feed efficiency (i) and aggression (p).See TableS13for corresponding FCB fuzzy sets for all traits.

Figure S3 .
Figure S3.Deviations in index values from the baseline estimate (all traits included) of food security (a, b), climate change (c, d), and biodiversity (e, f) following removal of traits as inputs to the fuzzy system.The black dots (a, c, e) and band in the middle of the box (b, d, f) are the median of the deviations of the 54 aquaculture species when attributes were removed (a, c, e) and an increasing number of attributes were randomly excluded (b, d, f).Violin plots in (a, c, e) show the minimum, maximum and distribution of deviations.The bottom and top of the boxplots in (b, d, f) show the 25th and 75th quartiles of the deviations, respectively.The lower and upper end of each vertical line represent the minimum and maximum values, while the black dots are outliers.Traits removed in (a), (c) and (e) are: A (Absolute fecundity); B (Aggression--trophic level); C (Bioremediation potential); D (Carbon sequestration potential); E (Feed conversion efficiency-trophic level); F (Geographic range); G (VBGF K); H (Latitudinal range); I (Macronutrient density); J (Maximum size); K (Micronutrient density); L (Nitrate range); M (Ocean acidification buffering potential); N (pH sensitivity); O (Phosphate range); P (pO2 tolerance); Q (Reproductive frequency); R (Salinity range); S (Spatial behavior); T (Structural provisioning potential); U (Temperature tolerance).

Table S7 .
Corresponding water area values in km² for each linguistic category of geographic range.

Table S10 .
Linguistic categories for micronutrient (a) and macronutrient (b) density, with corresponding percent contributions of each nutrient in 100 grams wet weight to RDA.

Table S13 .
Trait fuzzy sets and corresponding FCB contribution potential Corresponding fuzzy sets

Statistical tests on non-normal distributions of FCB scoresTable S15 .
Atlantic salmon has low potential to contribute to food security in terms of its trophic level (aggression, feed efficiency), pO2 range, nitrate range and absolute fecundity.These traits could be targeted to improve the food security potential of Atlantic salmon.By contrast, the VBGF (K), maximum size, nutrient density and phosphate ranges of Atlantic salmon indicate high food security potential.All traits considered, Atlantic salmon has a final food security index of 47.61, on a scale of 1 to 100.Shapiro-Wilk normality test results.A p-value < 0.05 indicates the observations vary significantly from a normal distribution.

Table S16 .
Kruskal-Wallis test results on the FCB scores.A p-value < 0.05 indicates that at least one taxonomic group has a significantly different median index from other groups.